Simulation Game Using Actual Organization Data

ABSTRACT

A technology is described for a simulation game. In one example of the technology, organization data can be obtained from an actual organization, and success metrics and derivative metrics that impact the success metrics may be identified. Organization roles may be assigned to players and a simulated scenario that is based on the organization data may be provided to the players. The players may be instructed to make decisions regarding the simulated scenario based on the roles assigned to the players, and the decision may be used to calculate values for the derivative metrics. The values of the derivative metrics may be aggregated to generate a value of a success metric for the organization. In the process, player behaviors, actions, and inactions may be used to build demonstrated performance-based behavioral profiles for each player within an organizational context.

RELATED APPLICATION

This application claims priority to U.S. Provisional Application No.62/752,239, filed Oct. 29, 2018, which is incorporated herein byreference.

BACKGROUND

A simulation game may generally be designed to closely simulate realworld activities. A simulation game attempts to copy various activitiesfrom actual life in the form of a game for various purposes such astraining, analysis, or prediction. A simulation game may be a replica ofreality. As a training program, a simulation game may allow players tolearn through interactive experiences. Simulations may contain elementsof experiential learning and adult learning. As such, simulations may beuseful for learning about complex situations, where the problems may beunfamiliar, and where the cost of errors in making decisions is likelyto be high. As such, simulation games may offer many benefits, such asaccelerating and compressing time to offer foresight for simulatedscenarios, promoting creativity among players who may develop a sharedview of the player's learning and behaviors, as well as other benefits.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram illustrating a high level example of a method forconstructing a simulation game to simulate an actual organization usingorganization data in accordance with an example of the presenttechnology.

FIGS. 2A-F illustrate example simulation game interfaces in accordancewith an example of the present technology.

FIG. 3 is a flow diagram that illustrates an example of a simulationgame and a simulated scenario in accordance with an example of thepresent technology.

FIGS. 4A-B illustrate one example of a simulation game constructed usingexample airline organization data in accordance with an example of thepresent technology.

FIG. 5 illustrates an example of an analytics dashboard in accordancewith an example of the present technology.

FIG. 6 is a block diagram that illustrates various example componentsincluded in a system for hosting a simulation game platform inaccordance with an example of the present technology.

FIG. 7 is a flow diagram that illustrates an example method for asimulation game for assessing player behavior in context of an actualorganization in accordance with an example of the present technology.

FIG. 8 is block diagram illustrating an example of a computing devicethat may be used to execute a simulation game in accordance with anexample of the present technology.

DETAILED DESCRIPTION

A technology is described for a simulation game used to evaluate playerbehavior in the context of an actual organization. The technology usesorganization data collected from an actual organization, such as abusiness, non-profit organization, educational institution, or any othertype of organization, and the organization data collected from theactual organization can be used to construct a simulation game thatrepresents in part the actual organization. As one example, organizationdata collected from an actual organization can be evaluated to identifyone or more categories of success metrics for the organization thatrepresent various areas of success of the organization. The organizationdata may then be evaluated to identify derivative metrics thatcollectively drive the success metrics (e.g., derivative metrics feedinto a success metric, such that the derivative metrics can beaggregated to determine the success metric). Thereafter, theorganization data may be used to construct a simulated scenario from thecontext of the actual organization. A simulated scenario may simulate anaspect of organizational management that involves organizationaldecision making. The simulated scenario can be provided to one or moreplayers who can be assigned organization roles that exist within theactual organization and are associated with the derivative metrics.

In one example, players can be instructed to make decisions that impactthe derivative metrics associated with the organization roles assignedto the players, and the decisions selected by the players can be used tocalculate the derivative metrics. The values of the derivative metricscan be aggregated to generate success metrics for the simulatedscenario, and the success metrics can be evaluated to determine whetherthe decisions selected by the players contributed to the success of thesimulated scenario.

In another example, players can be instructed to make decisions whichmay be within that organization's context and behavioral peopleanalytics data can be generated for the players by evaluating thedecisions. In some examples, the decisions may not impact a successmetric. As an illustration, a player may be asked for an opinion aboutan issue that is not related to a success metric, and the opinionprovided by the player can be analyzed to generate behavioral peopleanalytics data. In one example, demonstrated decisions made by a playerduring a simulated scenario can be evaluated and a behavioral profilecan be generated based on: the behaviors of the player, demonstrateddecisions made by the player, and/or inaction of the player (e.g., afailure to select a decision). A behavioral profile for a player canindicate at least one demonstrated behavioral attribute of the player.Simulation game output, such as quantitative data, qualitative data, andbehavioral profiles generated as part of executing simulated scenarioscan be exported to other human capital management (HCM) systems to allowthe simulation game output to be used in association with anorganization's employee decision making or people analytics in general.Moreover, practitioners may be provided with simulation game output andthe practitioners can use the behavioral people analytics output by thesimulation game to inform work including, but not limited to,assessment, learning, leadership selection, leadership development,organizational design, decision rights, recruiting and hiring.

The simulation game may be single player or multiplayer, and a player'sdecisions may factor into impacting the derivative metrics during gameplay. The simulation game may be configured to model historicaldecisions made by the organization, as well as to model alternativeoutcomes that the organization could have made, thereby allowing formultiple permutations (e.g., hundreds of thousands) of the gamesimulation. The simulation game may be configured based on currentdecisions made by the organization, and the organization's currentorganizational data, to project future possible decisions and outcomesthat the organization could make, thereby allowing for multiple forwardlooking permutations (e.g., hundreds of thousands) of the gamesimulation. Decisions presented to players may take numerous forms, suchas, multiple choice questions, budget allocations along a sliding scale,team choice voting, and more. Each player can make independent decisionsand those decisions aggregate together to impact a simulated scenario.Moreover, a number of players may simultaneously be provided withsimulated scenarios that are contextual to an actual organization. Thesimulated scenarios can be used to measure demonstrated behaviors andtraits of a player deemed to be valuable by the actual organization.

To further describe the present technology, examples are now providedwith reference to the figures. FIG. 1 is a diagram illustrating a highlevel example of a method used to construct a simulation game tosimulate various scenarios of an actual organization 102 usingorganization data. As used herein, an “actual organization” may refer toan existing entity that is organized for some stated purpose, includingbusinesses, non-profit organizations, educational institutions,governments, and the like. In one example, the simulation game can beused to evaluate player behavior in context of an organization 102. Inthe example illustrated, organization data 104 may be collected from anactual organization 102. The organization data 104 can include anyinformation associated with the organization 102, including organizationpolicies and procedures, financial data, operations data, departmentdata, organization structure information, and other organizationinformation. The organization data 104 may be collected via interviewswith organization personnel, observing day-to-day business processes,collecting data from organization databases, organization literature,promotional documents, corporate videos, press conferences, employeeevents, employee performance data, data archives, data intelligencesystems, as well as from any other data source containing organizationdata 104. The organization data 104 can provide a digital work sample ofthe organization 102 which can be used to drive simulated scenariospresented to players.

Organization data 104 for an organization 102 may be analyzed toidentify success metrics for the organization 102. Many organizationsuse success metrics to evaluate organization performance. Successmetrics may be a quantifiable measure used to track and assess thestatus of a specific organizational process. For example, anorganization's success metrics may track performance associated withsales revenue, profit margin, sales growth, cost of customeracquisition, customer satisfaction, employee satisfaction, and the like.Areas of an organization 102 may have specific performance metrics thatmay be monitored and these performance metrics may be thought of asderivative metrics which directly impact the success metrics of theorganization 102. For example, executives may track employee turnover,sales teams may monitor customer churn rate and return on investment incertain partnerships, and marketers may track marketing and social mediametrics, wherein each of these derivative metrics may impact one or moresuccess metrics of the larger organization. The success metrics of anorganization 102 may represent the organization's primary objectives,and the success metrics may be used to measure how well the organization102 may be doing at achieving the organization's primary objectives. Asan illustration, success metrics for an organization 102 may include,but are not limited to: sales revenue, net profit margin, gross margin,market growth, shareholder satisfaction, customer satisfaction, andemployee satisfaction. Success metrics may be different betweenorganizations based on the organization's primary objectives, and assuch, the derivative metrics (e.g. the performance that drivesorganizational success) may vary based on the organization. Thesimulation game can be constructed to simulate various scenarios of anorganization 102 using the success metrics and derivative metrics thatare particular to the organization 102.

A success metric may include a number of derivative metrics that drivethe success metric. The derivative metrics may directly and/orindirectly impact an organization's success metrics, and the derivativemetrics may collectively determine whether the organization 102 achievesthe organization's success metrics. As a non-limiting example, acustomer satisfaction metric may be determined, at least in part, byderivative metrics that include: operating revenue, operating margin,operating costs, equity growth, employee productivity, and employeemorale. Collectively, these derivative metrics may determine how wellthe organization is doing in the area of customer satisfaction.

Accordingly, the success metrics of the organization 102 may be analyzedto determine derivative metrics or derivative variables that drive theorganization's success metrics. Having collected the organization data104 and identified the success metrics of the organization 102, alongwith the derivative metrics that impact the success metrics, asimulation game 106 may be constructed using the organization data 104.In one example, constructing the simulation game 106 may include mappingorganization roles 108 a-n to derivative metrics. Examples oforganization roles 108 a-n include corporate roles (e.g., chiefexecutive officer (CEO), chief operating officer (COO), chief financialofficer (CFO), department manager, supervisors, etc.), company roles(e.g., owner, office manager, department lead, board of director, etc.),government roles (mayor, city manager, department manager, etc.). Theorganization roles 108 a-n may be associated with organization activity,functions, or decisions that generate the derivative metrics. Forexample, a CEO may make workforce decisions that impact employee morale,which may be a derivative metric of employee satisfaction and customersatisfaction. A CFO may make financial decisions that impactorganization finances, which may be a derivative metric of financialstability, reputation, and employee turnover. Thus, mapping organizationroles to derivative metrics, which drive organizational performancewithin a simulation game 106 allows players to make contextual decisionsthat may directly impact the derivative metrics. The player's decisionscan be aggregated to calculate organization performance, which can beused to evaluate the player's ability to make independent decisions thatfurther the organization's primary objectives.

Constructing a simulation game 106 may include constructing a pluralityof simulated scenarios that may be based in part on the organizationdata 104. The simulated scenarios may correlate to one or more successmetrics of the organization 102. A simulated scenario may simulate anorganization event (e.g., change of leadership or ownership, newlyenacted regulation affecting the organization, expansion or contractionof the organization, etc.) or crisis (employee strike, leadershipscandal, lawsuit, industrial accident, etc.). As an example, a simulatedscenario may comprise an employee strike and the simulation game 106 maymodel the employee strike using organization data 104. Players actingwithin various organization roles 108 a-n may be instructed to makedecisions to resolve the simulated employee strike, and the decisionsmade by the players may be evaluated to determine how the decisionsimpact the performance of the organization 102 to handle the simulatedemployee strike, which may be represented via the success metrics forthe organization 102.

After constructing the simulation game 106 using the organization data104, the simulation game 106 may be provided to players via a simulationgame interface. The simulation game 106 may be multiplayer or singleplayer. For example, a plurality of players may join an instance of thesimulation game 106 and simultaneously work through simulated scenarios,or a player may individually work through one or more simulatedscenarios, or the player may work through simulated scenarios with othersimulated players (e.g., interactive agents or chatbots). In oneexample, the simulation game 106 may be centrally hosted on a server andplayers may join the simulation game 106 using client devices, asdescribed in greater detail in FIG. 6. In another example, thesimulation game 106 may be hosted on a player's device, such as acomputer, workstation, mobile device, or the like.

As shown in FIGS. 2A-F, a simulation game interface for the simulationgame 106 may include a graphical user interface, and players may berepresented in the simulation game 106 using simulated players (e.g.,computer generated avatars, simulated characters, human representations,humanoids, animals, fantasy creatures, and the like). For example, FIG.2A illustrates a welcome screen of the simulation game 106. FIG. 2Billustrates a user interface that allows a player to select parametersused to construct a simulated scenario which can then be presented tothe player to work through. FIG. 2C illustrates a user interface thatpresents a simulated scenario to a player and presents options to theplayer that represent decisions the player is able to select from. FIGS.2D-E illustrate a user interface that presents options associated with asimulated scenario using sliding controls that allow a player to makedecisions by setting the sliding controls to a value. FIG. 2Fillustrates a user interface that includes an input control that allowsa player to explain a decision made by the player.

Returning to FIG. 1, as part of joining a simulation game 106, a playermay select, or be assigned, an organization role 108 a-n (e.g., CEO,COO, CFO, etc.), and the players may be provided with the organization'ssuccess metrics along with a simulated scenario that correlates to oneor more of the success metrics. The player may then be instructed tomake decisions regarding the simulated scenario based on the role 108a-n assigned to the player. The decisions may take numerous forms thatcan include multiple choice questions, budget allocations, team choicevoting, open response answers, peer-to-peer feedback, and the like. Inone example, a decision made by one player may define or alter asimulated scenario for another player, who may then make a decisionbased at least in part on the defined or altered simulated scenario.Each player can make independent decisions which can be used tocalculate derivative metrics 110 a-n, and the derivative metrics 110 a-nmay be aggregated to generate success metrics 112 for the organization102.

FIG. 3 illustrates one example of a simulated scenario 300 which can beprovided to players within the context of a simulation game. Thesimulated scenario 300 can include a workflow that a player is asked towork through by assuming an organization role and make decisions relatedto the organization role. Decisions made by the player may determine apath of the workflow, and may affect the organization roles of otherplayers, who can make decisions and/or perform projects based onworkflow of the simulated scenario 300.

FIGS. 4A-B illustrate a non-limiting example of a simulation gameconstructed using example organization data for an airline entity, wheresuccess metrics for the organization can include:

-   -   Customer satisfaction    -   Employee satisfaction    -   Shareholder satisfaction        and derivative metrics or variables that drive the success        metrics can include:    -   Relative Growth Rate (RGR)—Available seat miles in the current        period divided by available seat miles in a comparable period        from the year earlier.    -   Relative Load Factor (RLF)—How well the average individual        airplane is used. Simply stated, the load factor is that        proportion of an airplane's seats that are sold and actually        filled at departure.    -   Promotion Effectiveness (PE)—The effectiveness of the airline's        promotional expenditures.    -   Operating Revenue (OR)—Total operating revenue per available        seat mile.    -   Operating Margin (OM)—Total operating revenue per available seat        mile less total operating cost per available seat mile.    -   Operating Costs (OC)—Total operating costs per available seat        mile.    -   Equity Growth (EG)—Total equity of current period subtracted by        the total equity of the earlier period.    -   Employee Productivity (EP)—How effectively the employees work        together in providing the physical service of getting passengers        from one place to another.    -   Employee Morale (EM)—How committed employees are to providing        good service to the airline's customers.    -   Debt to Total Assets (DTA)—Long-term debt divided by total        assets at end of period.    -   Attractiveness (A)—Attractiveness of the airline's service.    -   Aircraft Utilization (AU)—How well the companies' major assets        (airplanes) are used as a group.        As shown, the success metrics may be assigned starting values        and the values may be modified based on the decisions made by        the players. For example, a player may be presented with a        number of options for a simulated scenario from which the player        can select. The options may represent weighted decisions that        the players can make, and the decisions may correlate to one or        more derivative metrics that drive the success metrics.        Accordingly, as the players select options representing        decisions, the weights assigned to the decisions can be applied        to the values of the success metrics, and the values of the        success metrics can be used to represent performance of the        organization as determined by the players.

As a non-limiting example that is based on the information shown inFIGS. 4A-B, the success metrics may be assigned a starting value of20.0. The derivative metrics may be calculated by weighting thederivative metrics and summing the derivative metrics. CustomerSatisfaction is the percent sum of 30% Attractiveness (A), 30% PromotionEffectiveness (PE), 20% Employee Morale (EM), and 20% Relative GrowthRate (RGR). Because the non-weighted values are 20, the weighted valuesare 6, 6, 4, and 4 respectively. Therefore, before making decisions,Customer Satisfaction is 20%. For the sake of concision, weightedvariables are written with the prime symbol (i.e. PE′ rather than PE).

1. Player 1 (CEO) selects the lay Off Ramp′ option, which zeroes out EM.20% of 0 is 0, so Customer Satisfaction is now 16%.

2. Player 2 (CFO) allocates 14 million to the CHRO, 11 million to theCMO, 10 million to the COO, and 11 million to their own role. In theproject allocation, they put 2 million into fuel hedging, 2 million intofleet size optimization, 3 million into an ERP system, and 4 millioninto average fare. None of the allocations affect metrics relevant toCustomer Satisfaction.

3. Player 3 (COO) allocates 1 million to lean services, 2 million to acompensation plan, 4 million to downsizing, and 3 million to six sigma.This reduces A by 3, bringing it to 17. 30% of 17 is 5.1, so CustomerSatisfaction is now 15.1%.

4. Player 4 (CHRO) allocates 2 million to optimization, 4 million toincentivization, 3 million to an ERP system, and 4 million to a talentretention plan. This brings A back up to 20, making A′ equal to 6. Thisalso increases the EM to 6. 20% of 6 is 1.20. These changes bringCustomer Satisfaction to a total of 17.2%.

5. Player 5 (CMO) allocates 3 million to ATL/BTL/TTL advertising, 3million to social media, 3 million to lobbying, and 2 million tocompetitor analysis. This makes A=31.3, A′=9.38; PE=31.3, PE′=9.38; andmakes EM=12.8, EM′=2.55. Therefore, Customer Satisfaction becomes 25.3%.

Accordingly, Customer Satisfaction is the percent sum of the fourweighted values: Attractiveness, 9.38; Promotion Effectiveness, 9.38;Employee Morale, 2.55; and Relative Growth Rate, 4.00. Therefore,Customer Satisfaction after the first round of decisions is 25.3%,wherein A′+PE′+EM′+RGR′=Customer Satisfaction which translates to9.38+9.38+2.55+4.00=25.3%.

Returning to FIG. 1, as part of playing the simulation game 106, playerbehavior may be evaluated within the context of the organization 102,and specific behaviors of the players may be analyzed to provide insightinto an individual's behaviors, characteristics, traits, attributes,styles, and preferences. Illustratively, player behavior that can beanalyzed may include: an amount of time for a player to select adecision, the decisions a player consistently prioritizes, the degreeand qualities of the decisions, the patterns of decision making orleadership style a player expresses to make decisions, a playerswitching a selected decision, resources used by the a player to selecta decision, communications between players, communications between aplayer and a simulated customer, inaction of a player to select adecision, as well as other behaviors.

As an example, players can be simultaneously placed in contextualexperiences to measure demonstrated behaviors of traits deemed valuableby an organization (e.g., an organization's “core competency model”).For example, players may include students and/or employees who may beplaced in a simulated environment using the organization's actual data,and the player's demonstrated performance can be analyzed within thecontext of that actual organization. By using an organization's actualdata, a cultural relevant and realistic simulation can be created thatallows the organization to have higher confidence that a student oremployee may have high potential talent within the organization. Forexample, an organization that seeks to identify employees and/orstudents who demonstrate attributes, such as customer focus, resiliency,strategic thinking, change agility, salesmanship, innovative thinking,loyalty, growth mindset, dependability and collaborative approach, mayutilize the technology to construct a simulation game and identifyplayers that exhibit these attributes. As a specific example, thesimulation game may be configured to expose a player to customers withincreasingly extreme demands, for which the player must make decisionsbased on finite constraints to prioritize a customer, a company, orfellow employees in order to derive a customer focus score for theplayer.

In one example, players may be provided with a simulated scenario andone or more of the players may be instructed to submit a writtendecision to the simulated scenario. The written decision may be providedto the other players who then vote on the written decision. As anexample, a player may be evaluated for strategic thinking skills tounderstand how the player's decisions operate across an organization byexposing the player to a recent challenge facing an organization oranother organization. The player may be instructed to write an openresponse to the challenge and the response may be evaluated by the otherplayers in the simulation across different organization roles. Theplayers may agree or disagree with the decision made in the response,which may be aggregated to a strategic thinking score. Also, thewillingness of players to switch their decisions to align with the restof their team may be measured, which may also be aggregated into astrategic thinking score. While each attribute and/or behavior of aplayer may be measured differently, the features associated with theattributes and behavior may be leveraged across many organizations whomay be seeking similar attributes.

Also, in one example, a simulation game may be constructed usingorganization data for an actual organization and the simulation game maybe provided to schools (e.g., business schools) and similar institutionswhere players may be exposed to the organization and allowed to makedecisions related to the organization, as well as explore potentialoutcomes that result from the decisions. Moreover, performance data forplayers can be captured, and the performance data can be shared withpotential employers. In one example, performance data for players can becollected and behavioral people analytics can be generated and displayedusing an analytics dashboard. FIG. 5 illustrates an example analyticsdashboard 502 which can be used to provide behavioral people analyticsthat are based on player performance data. An organization can use thebehavioral people analytics displayed in the analytics dashboard 502 todetermine and/or monitor a player's ability to navigate and respond toissues presented to the player in a simulated scenario. Also, theanalytics dashboard 502 can be used by employers to identify highpotential players who the employer may want to recruit for theemployer's business.

FIG. 6 illustrates components of an example system 600 on which thepresent technology may be executed. The system 600 may include one ormore servers 602 configured to host a simulation game platform 604. Aserver 602 may contain modules and data stores that comprise thesimulation game platform 604. In one example, a server 602 may belocated in a service provider environment (e.g., a “cloud” environment)and the server 602 may host the simulation game platform 604 within theservice provider environment, such that the simulation game platform 604may be available to clients 664 a-n via a network 622.

A client 664 a-n may include any device capable of sending and receivingdata over a network 622. A client 664 a-n may comprise, for example, aprocessor-based system such as a computing device. A client 664 a-n maybe a device such as, but not limited to, a desktop computer, laptop ornotebook computer, tablet computer, handheld computer, workstation,network computer, or other devices with like capability. The server 602may be in communication with the clients 664 a-n via a network 622.

In one example, a server 602 may include a user interface module 608 anda simulation module 606. The user interface module 608 may be configuredto provide simulation game output to clients 664 a-n and receivesimulation game input from the clients 664 a-n. For example, game outputmay include graphics data for a simulated environment (e.g., aconference room) and game play data that may include multiple choicequestions, project files, voting tools, and the like. In one example,the user interface module 608 may be used to provide a descriptionand/or a value of one or more success metrics 618 for an organizationfor display to users (e.g., players, observers, administrators, etc.).For example, at the start of the simulation game, players may beprovided with a description of the success metrics 618 so that theplayers can work towards the success metrics 618. At the end of asimulated scenario and/or at the end of game play, the value of thesuccess metrics 618 may be provided to the players, thereby providing ameasure of player performance.

The simulation module 606 may be configured to execute a simulation gameusing scenario data 612 and organization data 610. Illustratively, agame administrator, or a player, may initialize an instance of asimulation game and players may join the simulation game using clients664 a-n. In one example, players may be provided with a link (e.g., ahyperlink containing a uniform resource locator (URL) for the instanceof the simulation game) and the players can use the link to connect tothe instance of the simulation game. The simulation module 606 may beconfigured to assign organization roles 616 to the players, where theorganization roles 616 may be mapped to derivative metrics 620. Thesimulation module 606 may be configured to obtain scenario data 612 froma data store and generate a simulated scenario using the scenario data612. The simulated scenario may be based at least in part onorganization data 610 and the simulated scenario may correlate to one ormore success metrics 618 of an actual organization. The simulationmodule 606 may be configured to instruct the players, via the userinterface module 608, to make decisions regarding the simulated scenariobased on the organization roles 616 assigned to the players. The optionsprovided to the players may represent decisions that can be made by theplayers. The options can be weighted according to an impact on thederivative metrics 620. The simulation module 606 may be configured toreceive options selected by the players via the user interface module608, and the simulation module 606 may calculate values for thederivative metrics 620 according to the weights assigned to the options.The simulation module 606 may be configured to aggregate the derivativemetrics 620 to generate a value of one or more success metrics 618.

In addition, the simulation module 606 can be configured to modify thesimulation game based on the value of one or more success metrics 618and present a new simulated scenario to players based on theorganization data 610 and the value of the success metrics 618. As anillustration, decisions made by the players may result in a successmetric value (e.g., a poor customer service score) that may be belowperformance expectations. As a result, the simulation game can bemodified to present a new simulated scenario that represents the poorsuccess metric (e.g., a loss in revenue due to customerdissatisfaction).

The simulation module 606 may be configured to receive a decisionsregarding a simulated scenario from a first player assigned a first role(e.g., CEO), and instruct a second player assigned a second role (e.g.,CFO) to make a decision that is based at least in part on the decisionselected by the first player (e.g., CFO instructed to reallocatedepartment budget based on CEO decision to lay off employees).

The simulation module 606 can be configured to assign a project that isbased on organization data 610 to a player and evaluate performance ofthe player to perform the project using defined standards, and thesimulation module 606 can calculate a performance score that can beapplied to a corresponding derivative metric 620. For example, a playercan be assigned a budget project to be performed by allocating amountsusing a sliding scale. The budget project can be evaluated using definedstandards for how the amounts should be allocated, and a performancescore can be calculated based on how well the player allocated theamounts. The performance score can then be applied to one or morecorresponding derivative metrics 620.

The simulation module 606 may be configured to instruct a group ofplayers to make a group decision regarding a simulated scenario, wherethe group decision can be weighted according to an impact of the groupdecision on a derivative metric 620, and calculate a value for thederivative metric 620 using the group decision (the weight) selected bythe group of players. For example, the players can vote on a decisionand a derivative metric 620 can be calculated based on the vote. In oneexample, a decision selected by a first player can be provided to otherplayers and the other players can vote on the decision selected by thefirst player. The first player can then be instructed to select a finaldecision based in part on the votes of the other players. In oneexample, the first player may be asked to submit a written decision tothe simulated scenario and the written decision can be provided to theother players who vote on the written decision.

The simulation module 606 can be configured to analyze performance ofthe player to select a decision regarding the simulated scenario andcorrelate the performance to defined attributes (e.g., decision makingskills, collaboration skills, leadership skills, etc.) associated withone or more success metrics. Analyzing performance of the player furthermay include, but is not limited to, analyzing an amount of time for theplayer to select the decision, switching from one decision to anotherdecision, analyzing resources used by the player to select the decision,analyzing communications between the player and other players, andanalyzing communications between the player and a simulated customer.

In one example, the simulation module 606 can be configured to allow anon-player to join a simulation game and observe demonstratedperformance of the players. In one example, the simulation module 606can also be configured to allow a non-player to observe aggregatedreporting data on one or many users at once through a live orasynchronously generated report. For example, a user may wish to observethe players or the generated demonstrated performance reports in orderto evaluate the performance of the players for screening and/or hiringpurposes. In one example, the user may request to join the simulationgame (e.g., via the user interface module 608) and the user can beassigned a game profile that allows the user to observe the players inthe simulation game. In one example, the user may be represented in thesimulation game as a simulated player (e.g., computer generated avatar,simulated character, human representation, humanoid, animal, fantasycreature, and the like) which may be visible to the players, and in someexamples, the user may interact with the players. In another example,the user may not be visible to the players, but may observe the playerswithin the simulation game.

Also, in some examples, the simulation module 606 can be configured tocapture player data 614 generated during game play which can be used toconstruct at least one simulated scenario for use in an educationalenvironment in order to identify students that have attributes thatcorrespond to one or more success metrics 618 for an organization.Similarly, the player data 614 may be used to construct simulatedscenarios for use by other actual organizations that have attributesthat correspond to the success metrics 618 of the organization.

The various processes and/or other functionality contained within thesystem 600 may be executed on one or more processors that are incommunication with one or more memory modules. The system 600 mayinclude a number of computing devices that are arranged, for example, inone or more server banks or computer banks or other arrangements. In oneexample, the computing devices may support a service providerenvironment using hypervisors, virtual machine monitors (VMMs), andother virtualization software.

The system 600 may include data stores for storing organization data610, scenario data 612, player data 614, and other data. Organizationdata 610 may include data obtained from organization databases,organization literature, promotional documents, and other data. Theorganization data 610 may be analyzed to determine organization roles616, success metrics 618, and derivative metrics 620 for the particularorganization. The organization roles 616 may be actual positions heldwithin an organization. The success metrics 618 may represent primaryobjectives for an organization and may be assigned a score representingperformance of meeting the objectives. The derivative metrics 620 mayrepresent aspects of an organization that drive or impact the successmetrics 618 and the derivative metrics 620 may be assigned weights thatare based on an impact that the derivative metrics 620 have on thesuccess metrics 618. The term “data store” may refer to any device orcombination of devices capable of storing, accessing, organizing and/orretrieving data, which may include any combination and number of dataservers, relational databases, object oriented databases, clusterstorage systems, data storage devices, data warehouses, flat files anddata storage configuration in any centralized, distributed, or clusteredenvironment. The storage system components of the data store may includestorage systems such as a SAN (Storage Area Network), cloud storagenetwork, volatile or non-volatile RAM, optical media, or hard-drive typemedia. The data store may be representative of a plurality of datastores as can be appreciated.

The system 600 may use API calls, procedure calls, or other networkcommands that may be made in relation to the modules and servicesincluded in the system 600, and communications between the clients 664a-n and the simulation game platform 604. API calls may be implementedaccording to different technologies, including, but not limited to,Representational state transfer (REST) technology or Simple ObjectAccess Protocol (SOAP) technology. REST is an architectural style fordistributed hypermedia systems. A RESTful API (which may also bereferred to as a RESTful web service) is a web service API implementedusing HTTP and REST technology. SOAP is a protocol for exchanginginformation in the context of Web-based services.

The network 622 may include any useful computing network, including anintranet, the Internet, a local area network, a wide area network, awireless data network, or any other such network or combination thereof.Components utilized for such a system may depend at least in part uponthe type of network and/or environment selected. Communication over thenetwork may be enabled by wired or wireless connections and combinationsthereof.

While FIG. 6 illustrates an example of a system that may implement thetechniques above, many other similar or different environments arepossible. The example environments discussed and illustrated above aremerely representative and not limiting.

Moving now to FIG. 7, a flow diagram illustrates an example method 700for a simulation game used to evaluate player behavior within thecontext of an actual organization. In the past, methods of data captureand creation were structurally limited. Generally, self-reported datawas less reliable than demonstrated performance data. Further, certainhuman behaviors have different results in different organizationalconditions. It was also economically unfeasible to scale work samplesbeyond key executive assessments. While traditional testing mechanismscould be used to infer behavioral tendencies, directly observing thetendency in action negates having to infer these behavioral tendencies.The present technology can be used to overcome past restraints bycollecting employee work samples and other organization data from anactual organization by observing day-to-day business processes. Afterthe organization information has been collected, a user can be placedinto an organizational relevant game-based simulation that is based inpart on the organization data in order to obtain behavioral peopleanalytics for the user.

As in block 702, organization data may be obtained from an actualorganization, wherein the organization data is associated with at leastone success metric for the actual organization. As in block 704,derivative metrics that impact the at least one success metric may beidentified. For example, the organization data can be evaluated toidentify success metrics for the organization. After identifying thesuccess metrics, the organization data can be evaluated to identifyderivative metrics that drive the success metrics.

As in block 706, a role included in the actual organization may beassigned to at least one player, wherein the roles of the actualorganization can be mapped to the derivative metrics that impact the atleast one success metric. In one example, a player can be provided witha description of a success metric for an organization so that the playercan have an idea of what the player needs to achieve.

As in block 708, a simulated scenario may be provided to the at leastone player, wherein the simulated scenario is based at least in part onthe organization data and the simulated scenario correlates to the atleast one success metric of the actual organization. For example, aworkflow of the simulated scenario can be constructed to prompt a playerto make decisions that affect a success metric of the actualorganization by simulating an organization event (e.g., change ofleadership or ownership, newly enacted regulation affecting theorganization, expansion or contraction of the organization, etc.) orcrisis (employee strike, leadership scandal, lawsuit, industrialaccident, etc.).

As in block, 710, the at least one player can be instructed to makedecisions regarding the simulated scenario based on the role assigned tothe at least one player, wherein the decisions can be weighted accordingto an impact of the decisions on the derivative metrics. For example,weights can be assigned to options that are presented to a player, wherean option selected by the player can represent a decision made by theplayer.

As in block 712, the decisions selected by the at least one player maybe received, and as in block 714, values or scores can be calculated forthe derivative metrics using the decisions selected by the at least oneplayer. As in block 716, the derivative metrics can be aggregated togenerate a value of the at least one success metric. For example,weights assigned to options selected by players can be aggregated andapplied to a success metric to either increase or decrease the successmetric, and the ending value of the success metric can be used to gaugethe players' ability to handle the simulated scenario.

In one example, the performance of a player to select a decisionregarding a simulated scenario can be analyzed, and the performance ofthe player can be correlated to one or more defined attributesassociated with a success metric for an organization. Illustratively,analyzing performance of the player can include analyzing: an amount oftime for the at least one player to select the decision, switching aselected decision, resources used by the at least one player to selectthe decision, communications between the at least one player and otherplayers, or communications between the at least one player and asimulated customer.

In one example, a decision made by a first player assigned a particularrole (e.g., CEO) can be used to instruct a second player assigned adifferent role (e.g., CFO) to make a decision that is based, at least inpart, on the decision made by the first player. For example, a decisionregarding a simulated scenario can be received from the first player,and the second player can be instructed to make a decision based on thedecision selected by the first player.

In one example, a project can be assigned to players that can be based,at least in part, on organization data for an organization. Performanceof the players to perform the project can be evaluated and a performancescore can be calculated based on the performance of the players. Theperformance score can then be applied to a corresponding derivativemetric that drives a success metric.

In one example, a group of players can be instructed to make a groupdecision regarding a simulated scenario, where the group decision can beweighted according to an impact of the group decision on a derivativemetric. The group decision selected by the group of players can then beused to calculate a value for the derivative metric.

In one example, a decision selected by a first player to can be providedto other players. The other players can then vote on the decisionselected by the first player. For example, the first player can beinstructed to submit a written decision to the simulated scenario andthe written decision can be provided to the other players who vote onthe written decision. Thereafter, the first player can be instructed toselect a final decision based in part on the votes of the other players.

In one example, the simulation game can be modified based on the valueof a success metric, and a new simulated scenario can be presented tothe players based, at least in part, on organization data for anorganization and the value of the success metric. The players can thenmake decisions based on the new simulated scenario.

In some examples, a non-player may request to join a simulation game toobserve other players in the simulation game. In response to therequest, the non-player may be allowed to join the simulation game, andthe non-player can be assigned a game profile that allows the non-playerto observe the other players. In one example, the non-player may berepresented in the simulation game as, for example, a computer generatedavatar and the non-player can interact with the players. In anotherexample, the non-player may not be visible to the players, but mayobserve the players within the simulation game.

FIG. 8 illustrates a computing device 810 on which modules of thistechnology may execute. A computing device 810 is illustrated on which ahigh level example of the technology may be executed. The computingdevice 810 may include one or more processors 812 that are incommunication with memory devices 820. The computing device 810 mayinclude a local communication interface 818 for the components in thecomputing device. For example, the local communication interface 818 maybe a local data bus and/or any related address or control busses as maybe desired.

The memory device 820 may contain modules 824 that are executable by theprocessor(s) 812 and data for the modules 824. For example, the memorydevice 820 may include a simulation module, a user interface module, andother modules. The modules 824 may execute the functions describedearlier. A data store 822 may also be located in the memory device 820for storing data related to the modules 824 and other applications alongwith an operating system that is executable by the processor(s) 812.

Other applications may also be stored in the memory device 820 and maybe executable by the processor(s) 812. Components or modules discussedherein can be implemented in the form of software using high-levelprogramming languages that are compiled, interpreted, or executed usinga hybrid of the methods.

The computing device may also have access to I/O (input/output) devices814 that are usable by the computing devices. An example of an I/Odevice is a display screen 830 that is available to display output fromthe computing device 810. Networking devices 816 and similarcommunication devices may be included in the computing device. Thenetworking devices 816 may be wired or wireless networking devices thatconnect to the internet, a LAN, WAN, or other computing network.

The components or modules that are shown as being stored in the memorydevice 820 may be executed by the processor(s) 812. The term“executable” may mean a program file that is in a form that may beexecuted by a processor 812. For example, a program in a higher levellanguage may be compiled into machine code in a format that may beloaded into a random access portion of the memory device 820 andexecuted by the processor 812, or source code may be loaded by anotherexecutable program and interpreted to generate instructions in a randomaccess portion of the memory to be executed by a processor. Theexecutable program may be stored in any portion or component of thememory device 820. For example, the memory device 820 may be randomaccess memory (RAM), read only memory (ROM), flash memory, a solid statedrive, memory card, a hard drive, optical disk, floppy disk, magnetictape, or any other memory components.

The processor 812 may represent multiple processors and the memorydevice 820 may represent multiple memory units that operate in parallelto the processing circuits. This may provide parallel processingchannels for the processes and data in the system. The local interface818 may be used as a network to facilitate communication between any ofthe multiple processors and multiple memories. The local interface 818may use additional systems designed for coordinating communication suchas load balancing, bulk data transfer and similar systems.

While the flowcharts presented for this technology may imply a specificorder of execution, the order of execution may differ from what isillustrated. For example, the order of two more blocks may be rearrangedrelative to the order shown. Further, two or more blocks shown insuccession may be executed in parallel or with partial parallelization.In some configurations, one or more blocks shown in the flow chart maybe omitted or skipped. Any number of counters, state variables, warningsemaphores, or messages might be added to the logical flow for purposesof enhanced utility, accounting, performance, measurement,troubleshooting or for similar reasons.

Some of the functional units described in this specification have beenlabeled as modules, in order to more particularly emphasize theirimplementation independence. For example, a module may be implemented asa hardware circuit comprising custom VLSI circuits or gate arrays,off-the-shelf semiconductors such as logic chips, transistors, or otherdiscrete components. A module may also be implemented in programmablehardware devices such as field programmable gate arrays, programmablearray logic, programmable logic devices or the like.

Modules may also be implemented in software for execution by varioustypes of processors. An identified module of executable code may, forinstance, comprise one or more blocks of computer instructions, whichmay be organized as an object, procedure, or function. Nevertheless, theexecutables of an identified module need not be physically locatedtogether, but may comprise disparate instructions stored in differentlocations which comprise the module and achieve the stated purpose forthe module when joined logically together.

Indeed, a module of executable code may be a single instruction, or manyinstructions and may even be distributed over several different codesegments, among different programs and across several memory devices.Similarly, operational data may be identified and illustrated hereinwithin modules and may be embodied in any suitable form and organizedwithin any suitable type of data structure. The operational data may becollected as a single data set, or may be distributed over differentlocations including over different storage devices. The modules may bepassive or active, including agents operable to perform desiredfunctions.

The technology described here may also be stored on a computer readablestorage medium that includes volatile and non-volatile, removable andnon-removable media implemented with any technology for the storage ofinformation such as computer readable instructions, data structures,program modules, or other data. Computer readable storage media include,but is not limited to, a non-transitory machine readable storage medium,such as RAM, ROM, EEPROM, flash memory or other memory technology,CD-ROM, digital versatile disks (DVD) or other optical storage, magneticcassettes, magnetic tapes, magnetic disk storage or other magneticstorage devices, or any other computer storage medium which may be usedto store the desired information and described technology.

The devices described herein may also contain communication connectionsor networking apparatus and networking connections that allow thedevices to communicate with other devices. Communication connections arean example of communication media.

Communication media typically embodies computer readable instructions,data structures, program modules and other data in a modulated datasignal such as a carrier wave or other transport mechanism and includesany information delivery media. A “modulated data signal” means a signalthat has one or more of its characteristics set or changed in such amanner as to encode information in the signal. By way of example and notlimitation, communication media includes wired media such as a wirednetwork or direct-wired connection and wireless media such as acoustic,radio frequency, infrared and other wireless media. The term computerreadable media as used herein includes communication media.

Reference was made to the examples illustrated in the drawings andspecific language was used herein to describe the same. It willnevertheless be understood that no limitation of the scope of thetechnology is thereby intended. Alterations and further modifications ofthe features illustrated herein and additional applications of theexamples as illustrated herein are to be considered within the scope ofthe description.

Furthermore, the described features, structures, or characteristics maybe combined in any suitable manner in one or more examples. In thepreceding description, numerous specific details were provided, such asexamples of various configurations to provide a thorough understandingof examples of the described technology. It will be recognized, however,that the technology may be practiced without one or more of the specificdetails, or with other methods, components, devices, etc. In otherinstances, well-known structures or operations are not shown ordescribed in detail to avoid obscuring aspects of the technology.

Although the subject matter has been described in language specific tostructural features and/or operations, it is to be understood that thesubject matter defined in the appended claims is not necessarily limitedto the specific features and operations described above. Rather, thespecific features and acts described above are disclosed as exampleforms of implementing the claims. Numerous modifications and alternativearrangements may be devised without departing from the spirit and scopeof the described technology.

What is claimed is:
 1. A system for a simulation game for assessingplayer behavior in context of an actual organization, comprising: atleast one processor; a memory device including instructions that, whenexecuted by the at least one processor, cause the system to: assignroles of an actual organization to a plurality of players, wherein theroles are mapped to derivative metrics determined to impact at least onesuccess metric of the actual organization; present a simulated scenarioto the plurality of players, wherein the simulated scenario is based atleast in part on organization data and the simulated scenario correlatesto the at least one success metric of the actual organization; instructthe plurality of players to make decisions regarding the simulatedscenario based on the roles assigned to the plurality of players,wherein the decisions are weighted according to an impact of thedecisions on the derivative metrics; receive the decisions selected bythe plurality of players; calculate values for the derivative metricsusing the decisions selected by the plurality of player; and aggregatethe derivative metrics to generate a value of the at least one successmetric.
 2. The system as in claim 1, wherein the memory device includesinstructions that, when executed by the at least one processor, causethe system to: receive a decision regarding the simulated scenario froma first player assigned a first role; and instructing a second playerassigned a second role to make a decision that is based at least in parton the decision selected by the first player.
 3. The system as in claim1, wherein the memory device includes instructions that, when executedby the at least one processor, cause the system to: modify thesimulation game based on the value of the at least one success metric;and present a new simulated scenario to the plurality of players that isbased at least in part on the organization data and the value of the atleast one success metric.
 4. The system as in claim 1, furthercomprising a scenario data store for simulated scenarios that are basedat least in part on the organization data and the value of the at leastone success metric.
 5. A computer implemented method for a simulationgame, comprising: obtaining organization data from an actualorganization, wherein the organization data is associated with at leastone success metric for the actual organization; identifying derivativemetrics that impact the at least one success metric; assigning a roleincluded in the actual organization to at least one player, wherein theroles of the actual organization are mapped to the derivative metricsthat impact the at least one success metric; providing a simulatedscenario to the at least one player, wherein the simulated scenario isbased at least in part on the organization data and the simulatedscenario correlates to the at least one success metric of the actualorganization; instructing the at least one player to make decisionsregarding the simulated scenario based on the role assigned to the atleast one player, wherein the decisions are weighted according to animpact of the decisions on the derivative metrics; receiving thedecisions selected by the at least one player; calculating values forthe derivative metrics using the decisions selected by the at least oneplayer; and aggregating the derivative metrics to generate a value ofthe at least one success metric.
 6. The method as in claim 5, furthercomprising assigning starting values to the success metrics and applyingweights of the decisions selected by the at least one player to thevalues of the success metrics.
 7. The method as in claim 5, furthercomprising: assigning a project to the at least one player that is basedat least in part on the organization data; evaluating performance of theat least one player to perform the project; and calculating aperformance score that is applied to a corresponding derivative metric.8. The method as in claim 5, further comprising: instructing a group ofplayers to make a group decision regarding the simulated scenario,wherein the group decision is weighted according to an impact of thegroup decision on a derivative metric; and calculating a value for thederivative metric using the group decision selected by the group ofplayers.
 9. The method as in claim 5, further comprising: analyzingbehavior of the at least one player associated with selecting a decisionregarding the simulated scenario; and generating a behavioral profilefor the at least one player to indicate at least one demonstratedbehavioral attribute of the at least one player.
 10. The method as inclaim 9, wherein analyzing the behavior of the at least one playerfurther comprises analyzing: an amount of time for the at least oneplayer to select the decision, switching a selected decision, resourcesused by the at least one player to select the decision, a failure toselect a decision, communications between the at least one player andother players, or communications between the at least one player and asimulated customer.
 11. The method as in claim 5, further comprising:providing a decision selected by a first player to other players;receiving votes on the decision selected by the first player from theother players; and instructing the first player to select a finaldecision based in part on the votes of the other players.
 12. The methodas in claim 11, further comprising instructing the first player tosubmit a written decision to the simulated scenario that is provided tothe other players who vote on the written decision.
 13. The method as inclaim 5, further comprising: capturing player data generated during gameplay; and constructing at least one simulated scenario for use in aneducational environment to identify students that have attributes thatcorrespond to the at least one success metric for the actualorganization.
 14. The method as in claim 5, further comprising:capturing player data generated during game play; and constructing atleast one simulated scenario for use by other actual organizations thathave attributes that correspond to the at least one success metric forthe actual organization.
 15. A non-transitory machine readable storagemedium including instructions embodied thereon for a simulation game,the instructions when executed by one or more processors: assign rolesof an actual organization to a plurality of players, wherein the rolesare mapped to derivative metrics determined to impact at least onesuccess metric of the actual organization; retrieve a simulated scenariofrom a scenario data store that is based at least in part onorganization data and correlates to the at least one success metric ofthe actual organization; instruct the plurality of players to makedecisions regarding the simulated scenario based on the roles assignedto the plurality of players, wherein the decisions are weightedaccording to an impact of the decisions on the derivative metrics;receive the decisions selected by the plurality of players; calculatevalues for the derivative metrics using the decisions selected by theplurality of players; and aggregate the derivative metrics to generate avalue of the at least one success metric.
 16. The non-transitory machinereadable storage medium in claim 15, further comprising instructionsthat when executed by the one or more processors cause the one or moreprocessors to: generate a link containing a uniform resource locator(URL) for an instance of the simulation game; and send the link to aclient device associated with a player to allow the client device toconnect to the instance of the simulation game.
 17. The non-transitorymachine readable storage medium in claim 15, further comprisinginstructions that when executed by the one or more processors cause theone or more processors to generate a user interface to display thesimulated scenario to the plurality of players and display a list ofweighted options to the plurality of players, wherein a weighted optionselected by a player represents a decision of the player with respect tothe simulated scenario.
 18. The non-transitory machine readable storagemedium in claim 15, further comprising instructions that when executedby the one or more processors cause the one or more processors tofurther provide a description of the at least one success metric of theactual organization for display to the plurality of players.
 19. Thenon-transitory machine readable storage medium in claim 15, furthercomprising instructions that when executed by the one or more processorscause the one or more processors to further provide the value of the atleast one success metric for display to the plurality of players. 20.The non-transitory machine readable storage medium in claim 15, furthercomprising instructions that when executed by the one or more processorscause the one or more processors to further: receive a request from anon-player to join the simulation game; and join the non-player to thesimulation game, wherein the non-player is assigned a game profile thatallows the non-player to observe the plurality of players.